Design of an Efficient Genetic Algorithm to Solve the Industrial Car Sequencing Problem

Zinflou, Arnaud; Gagné, Caroline; Gravel, Marc

doi:10.5772/6122

Cited by 9 publications

(11 citation statements)

References 23 publications

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“…In the proposed implementation, the individuals of the initial population POP 0 are generated in two ways: 70 % randomly and 30 % using a greedy heuristic [20]. Notice that, in both cases, one ensures that the individuals produced are feasible solutions.…”

Section: Creating the Initial Populationmentioning

confidence: 99%

Solving the industrial car sequencing problem in a Pareto sense

Zinflou

Gagné

Gravel

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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Until now, the industrial car sequencing problem, as defined during the ROADEF 2005 Challenge, has been tackled by organizing objectives in a hierarchy. In this paper, we suggest tackling this problem in a Pareto sense for the first time. We thus suggest the adaptation of the PMS MO , an elitist evolutionary algorithm which distinguishes itself through a fitness calculation that takes into account the history of solutions found so as to diversify the compromise solutions along the Pareto frontier. A comparison of the performance is carried out using a well-known published algorithm, the NSGAII, and proves an advantage for the PMS MO . As well, we aim to demonstrate the relevance of handling applied problems such as the car sequencing problem using a multi-objective approach.

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Section: Creating the Initial Populationmentioning

confidence: 99%

Solving the industrial car sequencing problem in a Pareto sense

Zinflou

Gagné

Gravel

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

View full text Add to dashboard Cite

show abstract

“…A tie is broken by using an extension of a strategy introduced in (Gottlieb et al, 2003), which favours a better distribution of car classes in the sequence under construction. For more details on these subjects, see (Zinflou et al, 2008a).…”

Section: Genetic Phase 341 Crossover Operatormentioning

confidence: 99%

“…We chose the binary tournament selection because its implementation and execution costs are low, and because it has already been shown to work for the theoretical car sequencing problem (Zinflou et al, 2007) and the ICSP from the lexicographic viewpoint (Zinflou et al, 2008a). The tournament games are decided by the dominance factor of the participants, and a tie is broken by using the isolation factor.…”

Section: Selectionmentioning

confidence: 99%

“…Two greedy heuristics are used here: greedy_color and greedy_ratio. These two heuristics were first presented by Zinflou et al (2008a), to solve the ICSP lexicographically. Schematically, greedy_color minimizes the number of colour changes, whereas greedy_ratio minimizes the number of capacity conflicts for HPO's.…”

Section: Creating Of the Initial Populationmentioning

confidence: 99%

“…To solve the ICSP, the recombination operator we use is the NCPX MO introduced in (Zinflou et al, 2008a). Schematically, this operator tries to minimize the number of car classes to reposition in the sequence, by using the information linked to the conflictual positions in the parent sequence, based on the notion of Total Weighted Interest (TWI), which calculates whether a car of class v and colour Colors should be in position i in the sequence, according to the following equation: HPO HPO v,i,COLOR COLOR v,i,LPO LPO v,Colors,i = (4) where w HPO , w COLOR and w LPO correspond to the weights given to each objective (1000000, 1000 and 1 according to the objective priorities), and I v,i,HPO , I v,i,COLOR and I v,i,LPO correspond to the "interest" in placing the car class v in position i for each objective.…”

Section: Genetic Phase 341 Crossover Operatormentioning

confidence: 99%

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